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Quantum Bayesian Networks with Dynamic Connectivity

Jincheng Zhang

Published Sep 1, 2026• 0 citations
Publisher

Abstract

Quantum Bayesian Networks (QBNs) offer a powerful framework for modeling uncertainty and decision-making in complex systems. This paper explores the implementation of a novel QBN architecture that dynamically adjusts connectivity based on observed evidence, enhancing inference and decision-making capabilities. We introduce a dynamic connectivity mechanism that iteratively refines the network topology to better represent the underlying uncertainty. The resulting QBN exhibits improved performance in scenarios requiring adaptive decision-making compared to static network configurations. This work contributes to the advancement of Bayesian network inference and highlights the potential for more robust and flexible models.

DOI
10.5281/zenodo.22220721

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